Precise tumor diagnosis is the first step in cancer management since therapy generally stems from the initial tumor classification. While many tumor biopsies are diagnostic and form the cornerstone of cancer therapy, classification of tumor type and site of origin is a significant clinical challenge that is often under-estimated. Distinguishing the most common metastatic adenocarcinomas (ovary, colon, kidney, breast, lung and stomach) from each other is one of the most vexing problems facing clinicians today. In fact, it is estimated that up to 10% of all metastatic tumors have no defined primary site of origin. Moreover, adenocarcinomas represent 60% of all of unknown primary tumor types. The current standard of pathologic practice, using morphologic criteria and semi-quantitative immunohistochemical (IHC) analyses, is often limited in its capacity to define tumor type or site of origin. Thus, there is a clear need for the identification and validation of a classification model that will cleanly distinguish these histologically similar tumor types and improve the inventor's capacity to direct therapy.
Gene expression profiling is a powerful tool that has shown promise in its capacity to discriminate subpopulations of tumors from heterogeneous groups. The inventors recently developed a prototype multi-tumor classifier capable of interrogating up to 21 different tumor types with an accuracy of ˜88%. (see Bloom G, Yang I V, Boulware D, Kwong K Y, Coppola D, Eschrich S, Quackenbush J, Yeatman T J. Multi-platform, multi-site, microarray-based human tumor classification. Am J Pathol 2004; 164: 9-16; which is incorporated herein by reference).